'''
Adapted from kuangliu/pytorch-cifar .
'''

import torch.nn as nn
import torch
import numpy as np
import torch.nn.functional as F

# class ChannelAttention(nn.Module):
#     def __init__(self, in_planes, ratio=16):
#         super(ChannelAttention, self).__init__()
#         self.avg_pool = nn.AdaptiveAvgPool2d(1)
#         self.max_pool = nn.AdaptiveMaxPool2d(1)
#
#         self.fc = nn.Sequential(nn.Conv2d(in_planes, in_planes // 16, 1, bias=False),
#                                nn.ReLU(),
#                                nn.Conv2d(in_planes // 16, in_planes, 1, bias=False))
#         self.sigmoid = nn.Sigmoid()
#
#     def forward(self, x):
#         avg_out = self.fc(self.avg_pool(x))
#         max_out = self.fc(self.max_pool(x))
#         out = avg_out + max_out
#         return self.sigmoid(out)

class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size//2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        x = torch.cat([avg_out, max_out], dim=1)
        x = self.conv1(x)
        return self.sigmoid(x)

        
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)

        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)

        self.bn2 = nn.BatchNorm2d(planes)
        
        # self.ca = ChannelAttention(planes)
        self.sa = SpatialAttention()

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = self.conv1(x)

        out = self.relu(self.bn1(out))
        out = self.bn2(self.conv2(out))
        # out = self.ca(out) * out
        out = self.sa(out) * out
        out += self.shortcut(x)
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, in_channels=1, num_classes=5):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.linear = nn.Linear(512, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        fea = out
        out = self.relu(self.bn1(out))
        out = self.maxpool(out)

        out = self.layer1(out)
        fea1 = out

        out = self.layer2(out)
        fea2 = out

        out = self.layer3(out)
        fea3 = out

        out = self.layer4(out)
        fea4 = out

        # out = F.avg_pool2d(out, 4)
        out = self.avgpool(out)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def ResNet18(in_channels, num_classes):
    return ResNet(BasicBlock, [2, 2, 2, 2], in_channels=in_channels, num_classes=num_classes)
